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| Registros recuperados : 82 | |
| 1. |  | MOLLINARI, M.; MARGARIDO, G. R. A.; GARCIA, A. A. F. Comparação dos algoritmos delineação rápida em cadeia e seriação, para a construção de mapas genéticos. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 43, n. 4, p. 505-512, abr. 2008 Título em inglês: Comparison of algorithms rapid chain delineation and seriation, for the construction of genetic linkage maps.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 4. | | LARA, L. A. DE C.; SANTOS, M. F.; JANK, L.; CHIARI, L.; GARCIA, A. A. F. Modeling the variance-covariance matrix of genetic and residual effects in a Panicum maximum genome wide selection experiment. In: INTERNATIONAL CONFERENCE ON QUANTITATIVE GENETICS, 5., Madison, Wisconsin, USA, 2016. Proceedings... Madison, Wisconsin, USA: ICQG, 2016.| Biblioteca(s): Embrapa Gado de Corte. |
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| 5. | | SANTOS, M. A.; GERALDI, I. O.; GARCIA, A. A. F.; BORTOLATTO, N.; SCHIAVON, A.; HUNGRIA, M. Mapping of QTLs associated with biological nitrogen fixation traits in soybean. Hereditas, Lund, v. 150, n. 2-3, p. 17-25, 2013.| Biblioteca(s): Embrapa Soja. |
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| 6. | | ALVES, R. M.; GARCIA, A. A. F.; CRUZ, E. D.; FIGUEIRA, A. Seleção de descritores botânico-agronômicos para caracterização de germoplasma de cupuaçuzeiro. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 38, n. 7, p. 807-818, jul. 2003. il.| Biblioteca(s): Embrapa Amazônia Oriental; Embrapa Unidades Centrais. |
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| 9. | | FERRÃO, L. F. V.; FERRÃO, R. G.; FERRAO, M. A. G.; FONSECA, A. F. A. da; GARCIA, A. A. F. Mixed model to multiple havest-location trial applied to genomic prediction in Coffea canephora. In: PLANT & ANIMAL GENOME CONFERENCE, 24., 2016, San Diego, CA. [Abstracts...]. San Diego, CA: [s.n.], 2016.| Biblioteca(s): Embrapa Café. |
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| 10. | | BRAGA, M. F.; VIEIRA, M. L. C.; GAZAFFI, R.; GARCIA, A. A. F.; GIRALDI, I. O.; JUNQUEIRA, N. T. V. Mapeamento de QTL (quantitative trait loci) associados à resistência do maracujá-doce à bacteriose. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF, 2012.| Biblioteca(s): Embrapa Cerrados. |
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| 13. | | FAVERO, A. P.; MORAES, S. A. de; GARCIA, A. A. F.; VALLS, J. F. M.; VELLO, N. A. Characterization of rust, early and late leaf spot resistance in wild and cultivated peanut germplasm. Scientia Agricola, v.66, n. 1, p.110-117, 2009.| Biblioteca(s): Embrapa Recursos Genéticos e Biotecnologia. |
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| 14. | | CASTRO, C. M.; FERRÃO, L. F. V.; ROHR, A.; PINHEIRO, N. L.; PEREIRA, A. da S.; GARCIA, A. A. F. Population structure of potato breeding germplasm from Embrapa-Brazil assessed with single nucleotide polymorphism (SNP) markers. In: CONGRESO DE LA ASOCIACION LATINOAMERICANA DE LA PAPA - ALAP, 28., 2018, Cusco, Peru. Abstract Book 10th: Congress: Biodiversity, Food Security and Business. Instituto Nacional de Innovación: Agraria-INIA. Cusco, Perú, 2018. p. 123| Biblioteca(s): Embrapa Clima Temperado. |
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| 15. | | FÁVERO, A. P.; MORAES, S. A. de; GARCIA, A. A. F.; VALLS, J. F. M.; VELLO, N. A. Caracterização da resistÊncia à ferrugem, mancha preta e mancha castanha em germoplasma silvestre e cultivado de amendoim. Scientia Agricola, v. 66, n.1, p. 110-117, jan./fev., 2009.| Biblioteca(s): Embrapa Algodão. |
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| 16. | | ROSÁRIO, M. F. do; GAZAFFI, R.; MOURA, A. S. A. M. T.; LEDUR, M. C.; GARCIA, A. A. F.; COUTINHO, L. L. Base genética da correlação entre características de desempenho e de rendimento de carcaça no cromossomo 1 da galinha. In: CONFERÊNCIA FACTA DE CIÊNCIA E TECNOLOGIA AVÍCOLAS, 2011, Santos, SP. Anais... Santos: FACTA, 2011. Trabalhos de Pesquisa José Maria Lamas da Silva. 1 CD-ROM. Projeto: 02.09.07.006.| Biblioteca(s): Embrapa Suínos e Aves. |
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| 17. | | DIAS, K. O. G.; SANTOS, J. P. R. dos; KRAUSE, M. D.; PIEPHO, H.-P.; GUIMARAES, L. J. M.; PASTINA, M. M.; GARCIA, A. A. F. Leveraging probability concepts for cultivar recommendation in multi?environment trials. Theoretical and Applied Genetics, v. 135, n. 4, p. 1385-1399, 2022.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 18. | | VIGNA, B. B. Z.; JUNGMANN, L.; ALLEONI, G. C.; VALLE, C. B. do; FEIJO, G. L. D.; GARCIA, A. A. F.; SOUZA, A. P. Análise da segregação de locos microssatélites em uma população F1 segregante de Brachiaria humidicola hexaplóide. In: CONGRESSO BRASILEIRO DE GENÉTICA, 56., 2010, Guarujá. Resumos... Ribeirão Preto: Sociedade Brasileira de Genética, 2010. p. 176| Biblioteca(s): Embrapa Gado de Corte. |
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| 19. | | FREITAS, E. G. de; PASTINA, M. M.; GAZAFFI, R.; PINTO, L. R.; XAVIER, M. A.; LANDELL, M. G. de A.; GARCIA, A. A. F. Modelos mistos para seleção de genótipos superiores e de futuros genitores de cana-de-açúcar. In: REUNIÃO ANUAL DA REGIÃO BRASILEIRA DA SOCIEDADE INTERNACIONAL DE BIOMETRIA, 58.; SIMPÓSIO DE ESTATÍSTICA APLICADA À EXPERIMENTAÇÃO AGRONÔMICA, 15., 2013, Campina Grande. Modelagem estatística em áreas multidisciplinares: impactos causados pelas mudanças climáticas na Região Nordeste: anais. Campina Grande: Sociedade Internacional de Biometria, 2013.| Biblioteca(s): Embrapa Milho e Sorgo. |
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| 20. | | PÉREZ-JARAMILLO, J. E.; CARRIÓN, V. J.; BOSSE, M.; FERRÃO, L. F. V.; HOLLANDER, M. de; GARCIA, A. A. F.; RAMIREZ, C. A.; MENDES, R.; RAAIJMAKER, J. M. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. The ISME Journal, v. 11, p. 2244-2257, 2017.| Biblioteca(s): Embrapa Meio Ambiente. |
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| Registros recuperados : 82 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
24/07/2018 |
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Data da última atualização: |
05/02/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
DIAS, K. O. das G.; GEZAN, S. A.; GUIMARÃES, C. T.; NAZARIAN, A.; SILVA, L. da C. e; PARENTONI, S. N.; GUIMARAES, P. E. de O.; ANONI, C. de O.; PÁDUA, J. M. V.; PINTO, M. de O.; NODA, R. W.; RIBEIRO, C. A. G.; MAGALHAES, J. V. de; GARCIA, A. A. F.; SOUZA, J. C. de; GUIMARAES, L. J. M.; PASTINA, M. M. |
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Afiliação: |
Kaio Olímpio das Graças Dias, Universidade Federal de Lavras; Salvador Alejandro Gezan, School of Forest Resources & Conservation, University of Florida, Gainesville.; CLAUDIA TEIXEIRA GUIMARAES, CNPMS; Alireza Nazarian, School of Forest Resources & Conservation, University of Florida, Gainesville.; Luciano da Costa e Silva, JMP Division, SAS Institute Inc., Cary.; SIDNEY NETTO PARENTONI, CNPMS; PAULO EVARISTO DE O GUIMARAES, CNPMS; Carina de Oliveira Anoni, Escola Superior de Agricultura “Luiz de Queiroz”; José Maria Villela Pádua, Universidade Federal de Lavras; MARCOS DE OLIVEIRA PINTO, CNPMS; ROBERTO WILLIANS NODA, CNPMS; Carlos Alexandre Gomes Ribeiro, Universidade Federal de Viçosa; JURANDIR VIEIRA DE MAGALHAES, CNPMS; Antonio Augusto Franco Garcia, Escola Superior de Agricultura “Luiz de Queiroz”; João Cândido de Souza, Universidade Federal de Lavras; LAURO JOSE MOREIRA GUIMARAES, CNPMS; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Heredity, London, v. 121, n. 1, p. 24-37, 2018. |
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DOI: |
10.1038/s41437-018-0053-6 |
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Idioma: |
Inglês |
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Conteúdo: |
Breeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids. MenosBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS a... Mostrar Tudo |
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Thesagro: |
Milho; Resistência a Seca. |
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Categoria do assunto: |
-- |
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Marc: |
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100 1 $aDIAS, K. O. das G.
245 $aImproving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.$h[electronic resource]
260 $c2018
520 $aBreeding for drought tolerance is a challenging task that requires costly, extensive, and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here, we evaluated the accuracy of genomic selection (GS) using additive (A) and additive + dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multienvironment trials. Phenotypic data of five drought tolerance traits were measured in 308 hybrids along eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids? genotypes were inferred based on their parents? genotypes (inbred lines) using single-nucleotide polymorphism markers obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Two cross-validation (CV) schemes were tested: CV1 and CV2. The FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive-by-environment and the dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Results showed differences in the predictive accuracy between A and AD models, using both CV1 and CV2, for the five traits in both water conditions. For grain yield (GY) under WS and using CV1, the AD model doubled the predictive accuracy in comparison to the A model. Through CV2, GS models benefit from borrowing information of correlated trials, resulting in an increase of 40% and 9% in the predictive accuracy of GY under WS for A and AD models, respectively. These results highlight the importance of multi-environment trial analyses using GS models that incorporate additive and dominance effects for genomic predictions of GY under drought in maize single-cross hybrids.
650 $aMilho
650 $aResistência a Seca
700 1 $aGEZAN, S. A.
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700 1 $aNAZARIAN, A.
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773 $tHeredity, London$gv. 121, n. 1, p. 24-37, 2018.
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